Overview

Dataset statistics

Number of variables31
Number of observations563777
Missing cells3006921
Missing cells (%)17.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory133.3 MiB
Average record size in memory248.0 B

Variable types

Numeric20
DateTime1
Categorical4
Text6

Alerts

CANCELLED is highly imbalanced (98.4%)Imbalance
DIVERTED is highly imbalanced (98.9%)Imbalance
CANCELLATION_CODE has 562958 (99.9%) missing valuesMissing
CARRIER_DELAY has 487430 (86.5%) missing valuesMissing
WEATHER_DELAY has 487430 (86.5%) missing valuesMissing
NAS_DELAY has 487430 (86.5%) missing valuesMissing
SECURITY_DELAY has 487430 (86.5%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 487430 (86.5%) missing valuesMissing
WEATHER_DELAY is highly skewed (γ1 = 26.30717485)Skewed
SECURITY_DELAY is highly skewed (γ1 = 28.01922379)Skewed
DEP_DELAY has 27141 (4.8%) zerosZeros
ARR_DELAY has 10405 (1.8%) zerosZeros
CARRIER_DELAY has 31823 (5.6%) zerosZeros
WEATHER_DELAY has 73995 (13.1%) zerosZeros
NAS_DELAY has 38489 (6.8%) zerosZeros
SECURITY_DELAY has 75722 (13.4%) zerosZeros
LATE_AIRCRAFT_DELAY has 40437 (7.2%) zerosZeros

Reproduction

Analysis started2024-03-30 05:46:04.796411
Analysis finished2024-03-30 05:48:41.158276
Duration2 minutes and 36.36 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9520058
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:41.266147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9647226
Coefficient of variation (CV)0.49714567
Kurtosis-1.1716324
Mean3.9520058
Median Absolute Deviation (MAD)2
Skewness0.05944324
Sum2228050
Variance3.8601348
MonotonicityIncreasing
2024-03-30T02:48:41.501510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 93769
16.6%
4 90051
16.0%
1 79441
14.1%
7 79255
14.1%
5 76509
13.6%
2 74686
13.2%
6 70066
12.4%
ValueCountFrequency (%)
1 79441
14.1%
2 74686
13.2%
3 93769
16.6%
4 90051
16.0%
5 76509
13.6%
6 70066
12.4%
7 79255
14.1%
ValueCountFrequency (%)
7 79255
14.1%
6 70066
12.4%
5 76509
13.6%
4 90051
16.0%
3 93769
16.6%
2 74686
13.2%
1 79441
14.1%
Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Minimum2023-11-01 00:00:00
Maximum2023-11-30 00:00:00
2024-03-30T02:48:41.784400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:42.112768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
WN
122501 
DL
80353 
AA
76407 
UA
57897 
OO
57194 
Other values (10)
169425 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1127554
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 122501
21.7%
DL 80353
14.3%
AA 76407
13.6%
UA 57897
10.3%
OO 57194
10.1%
NK 23164
 
4.1%
YX 22002
 
3.9%
B6 21281
 
3.8%
MQ 19729
 
3.5%
AS 19255
 
3.4%
Other values (5) 63994
11.4%

Length

2024-03-30T02:48:42.383240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 122501
21.7%
dl 80353
14.3%
aa 76407
13.6%
ua 57897
10.3%
oo 57194
10.1%
nk 23164
 
4.1%
yx 22002
 
3.9%
b6 21281
 
3.8%
mq 19729
 
3.5%
as 19255
 
3.4%
Other values (5) 63994
11.4%

Most occurring characters

ValueCountFrequency (%)
A 236587
21.0%
N 145665
12.9%
O 130538
11.6%
W 122501
10.9%
D 80353
 
7.1%
L 80353
 
7.1%
U 57897
 
5.1%
9 32283
 
2.9%
K 23164
 
2.1%
H 22771
 
2.0%
Other values (11) 195442
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1127554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 236587
21.0%
N 145665
12.9%
O 130538
11.6%
W 122501
10.9%
D 80353
 
7.1%
L 80353
 
7.1%
U 57897
 
5.1%
9 32283
 
2.9%
K 23164
 
2.1%
H 22771
 
2.0%
Other values (11) 195442
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1127554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 236587
21.0%
N 145665
12.9%
O 130538
11.6%
W 122501
10.9%
D 80353
 
7.1%
L 80353
 
7.1%
U 57897
 
5.1%
9 32283
 
2.9%
K 23164
 
2.1%
H 22771
 
2.0%
Other values (11) 195442
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1127554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 236587
21.0%
N 145665
12.9%
O 130538
11.6%
W 122501
10.9%
D 80353
 
7.1%
L 80353
 
7.1%
U 57897
 
5.1%
9 32283
 
2.9%
K 23164
 
2.1%
H 22771
 
2.0%
Other values (11) 195442
17.3%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5923
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2292.2298
Minimum1
Maximum8817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:42.659462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile286
Q11044
median2047
Q33339
95-th percentile5371
Maximum8817
Range8816
Interquartile range (IQR)2295

Descriptive statistics

Standard deviation1560.0442
Coefficient of variation (CV)0.68057932
Kurtosis-0.55960755
Mean2292.2298
Median Absolute Deviation (MAD)1097
Skewness0.61927368
Sum1.2923064 × 109
Variance2433737.9
MonotonicityNot monotonic
2024-03-30T02:48:42.940017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
540 323
 
0.1%
323 313
 
0.1%
759 291
 
0.1%
1173 289
 
0.1%
648 289
 
0.1%
706 288
 
0.1%
649 288
 
0.1%
533 285
 
0.1%
519 283
 
0.1%
1515 280
 
< 0.1%
Other values (5913) 560848
99.5%
ValueCountFrequency (%)
1 169
< 0.1%
2 118
< 0.1%
3 106
< 0.1%
4 146
< 0.1%
5 145
< 0.1%
6 85
< 0.1%
7 118
< 0.1%
8 154
< 0.1%
9 150
< 0.1%
10 116
< 0.1%
ValueCountFrequency (%)
8817 1
< 0.1%
8815 1
< 0.1%
8814 1
< 0.1%
8810 1
< 0.1%
8809 1
< 0.1%
8800 1
< 0.1%
8790 2
< 0.1%
8788 1
< 0.1%
8786 1
< 0.1%
8785 2
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct332
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12648.151
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:43.216225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1529.3126
Coefficient of variation (CV)0.12091195
Kurtosis-1.3005151
Mean12648.151
Median Absolute Deviation (MAD)1591
Skewness0.10941036
Sum7.1307364 × 109
Variance2338797.1
MonotonicityNot monotonic
2024-03-30T02:48:43.540970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 27473
 
4.9%
11298 24070
 
4.3%
11292 23934
 
4.2%
13930 20354
 
3.6%
11057 16708
 
3.0%
12889 16234
 
2.9%
14107 15792
 
2.8%
12892 15742
 
2.8%
13204 14325
 
2.5%
12953 13264
 
2.4%
Other values (322) 375881
66.7%
ValueCountFrequency (%)
10135 374
 
0.1%
10136 141
 
< 0.1%
10140 2005
0.4%
10141 59
 
< 0.1%
10146 60
 
< 0.1%
10155 87
 
< 0.1%
10157 149
 
< 0.1%
10158 325
 
0.1%
10165 9
 
< 0.1%
10170 57
 
< 0.1%
ValueCountFrequency (%)
16869 136
 
< 0.1%
16218 144
 
< 0.1%
15991 58
 
< 0.1%
15919 986
0.2%
15841 58
 
< 0.1%
15624 642
0.1%
15607 60
 
< 0.1%
15582 51
 
< 0.1%
15569 51
 
< 0.1%
15412 1155
0.2%

ORIGIN
Text

Distinct332
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:44.237065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1691331
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLGA
2nd rowLGA
3rd rowTYS
4th rowLGA
5th rowJFK
ValueCountFrequency (%)
atl 27473
 
4.9%
dfw 24070
 
4.3%
den 23934
 
4.2%
ord 20354
 
3.6%
clt 16708
 
3.0%
las 16234
 
2.9%
phx 15792
 
2.8%
lax 15742
 
2.8%
mco 14325
 
2.5%
lga 13264
 
2.4%
Other values (322) 375881
66.7%
2024-03-30T02:48:45.465651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 192700
 
11.4%
L 157359
 
9.3%
S 144538
 
8.5%
D 131421
 
7.8%
T 90221
 
5.3%
O 85956
 
5.1%
C 85401
 
5.0%
M 76276
 
4.5%
F 70495
 
4.2%
W 66380
 
3.9%
Other values (16) 590584
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 192700
 
11.4%
L 157359
 
9.3%
S 144538
 
8.5%
D 131421
 
7.8%
T 90221
 
5.3%
O 85956
 
5.1%
C 85401
 
5.0%
M 76276
 
4.5%
F 70495
 
4.2%
W 66380
 
3.9%
Other values (16) 590584
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 192700
 
11.4%
L 157359
 
9.3%
S 144538
 
8.5%
D 131421
 
7.8%
T 90221
 
5.3%
O 85956
 
5.1%
C 85401
 
5.0%
M 76276
 
4.5%
F 70495
 
4.2%
W 66380
 
3.9%
Other values (16) 590584
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 192700
 
11.4%
L 157359
 
9.3%
S 144538
 
8.5%
D 131421
 
7.8%
T 90221
 
5.3%
O 85956
 
5.1%
C 85401
 
5.0%
M 76276
 
4.5%
F 70495
 
4.2%
W 66380
 
3.9%
Other values (16) 590584
34.9%
Distinct326
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:46.155588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.065255
Min length8

Characters and Unicode

Total characters7365890
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowNew York, NY
3rd rowKnoxville, TN
4th rowNew York, NY
5th rowNew York, NY
ValueCountFrequency (%)
ca 61128
 
4.6%
tx 60554
 
4.6%
fl 52257
 
4.0%
ny 30488
 
2.3%
san 29549
 
2.2%
ga 29397
 
2.2%
il 28118
 
2.1%
new 27955
 
2.1%
atlanta 27473
 
2.1%
chicago 27099
 
2.1%
Other values (396) 941286
71.6%
2024-03-30T02:48:47.184792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
751527
 
10.2%
, 563777
 
7.7%
a 563069
 
7.6%
o 406070
 
5.5%
e 389239
 
5.3%
n 360089
 
4.9%
t 351439
 
4.8%
l 326681
 
4.4%
i 279504
 
3.8%
r 268245
 
3.6%
Other values (46) 3106250
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7365890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
751527
 
10.2%
, 563777
 
7.7%
a 563069
 
7.6%
o 406070
 
5.5%
e 389239
 
5.3%
n 360089
 
4.9%
t 351439
 
4.8%
l 326681
 
4.4%
i 279504
 
3.8%
r 268245
 
3.6%
Other values (46) 3106250
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7365890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
751527
 
10.2%
, 563777
 
7.7%
a 563069
 
7.6%
o 406070
 
5.5%
e 389239
 
5.3%
n 360089
 
4.9%
t 351439
 
4.8%
l 326681
 
4.4%
i 279504
 
3.8%
r 268245
 
3.6%
Other values (46) 3106250
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7365890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
751527
 
10.2%
, 563777
 
7.7%
a 563069
 
7.6%
o 406070
 
5.5%
e 389239
 
5.3%
n 360089
 
4.9%
t 351439
 
4.8%
l 326681
 
4.4%
i 279504
 
3.8%
r 268245
 
3.6%
Other values (46) 3106250
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:47.644219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1394771
Min length4

Characters and Unicode

Total characters4588850
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNew York
3rd rowTennessee
4th rowNew York
5th rowNew York
ValueCountFrequency (%)
california 61128
 
9.5%
texas 60554
 
9.4%
florida 52257
 
8.1%
new 44463
 
6.9%
york 30488
 
4.7%
georgia 29397
 
4.6%
carolina 29304
 
4.5%
illinois 28118
 
4.4%
colorado 26031
 
4.0%
north 25938
 
4.0%
Other values (51) 257822
39.9%
2024-03-30T02:48:48.449884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 619650
13.5%
i 515146
 
11.2%
o 441621
 
9.6%
n 337058
 
7.3%
r 336909
 
7.3%
e 280895
 
6.1%
s 258441
 
5.6%
l 255059
 
5.6%
C 118137
 
2.6%
d 114157
 
2.5%
Other values (37) 1311777
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4588850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 619650
13.5%
i 515146
 
11.2%
o 441621
 
9.6%
n 337058
 
7.3%
r 336909
 
7.3%
e 280895
 
6.1%
s 258441
 
5.6%
l 255059
 
5.6%
C 118137
 
2.6%
d 114157
 
2.5%
Other values (37) 1311777
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4588850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 619650
13.5%
i 515146
 
11.2%
o 441621
 
9.6%
n 337058
 
7.3%
r 336909
 
7.3%
e 280895
 
6.1%
s 258441
 
5.6%
l 255059
 
5.6%
C 118137
 
2.6%
d 114157
 
2.5%
Other values (37) 1311777
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4588850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 619650
13.5%
i 515146
 
11.2%
o 441621
 
9.6%
n 337058
 
7.3%
r 336909
 
7.3%
e 280895
 
6.1%
s 258441
 
5.6%
l 255059
 
5.6%
C 118137
 
2.6%
d 114157
 
2.5%
Other values (37) 1311777
28.6%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.814159
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:48.779223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median45
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.643041
Coefficient of variation (CV)0.48606129
Kurtosis-1.3228245
Mean54.814159
Median Absolute Deviation (MAD)23
Skewness-0.032845138
Sum30902962
Variance709.85162
MonotonicityNot monotonic
2024-03-30T02:48:49.079922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 61128
 
10.8%
74 60554
 
10.7%
33 52257
 
9.3%
22 30488
 
5.4%
34 29397
 
5.2%
41 28118
 
5.0%
82 26031
 
4.6%
36 24558
 
4.4%
38 18747
 
3.3%
81 18080
 
3.2%
Other values (42) 214419
38.0%
ValueCountFrequency (%)
1 2441
 
0.4%
2 10584
1.9%
3 2923
 
0.5%
4 456
 
0.1%
5 99
 
< 0.1%
11 1674
 
0.3%
12 990
 
0.2%
13 11240
2.0%
14 505
 
0.1%
15 1050
 
0.2%
ValueCountFrequency (%)
93 15315
 
2.7%
92 6423
 
1.1%
91 61128
10.8%
88 542
 
0.1%
87 9412
 
1.7%
86 2193
 
0.4%
85 17843
 
3.2%
84 1640
 
0.3%
83 2347
 
0.4%
82 26031
4.6%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct332
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12648.242
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:49.371998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1529.2571
Coefficient of variation (CV)0.12090669
Kurtosis-1.3004231
Mean12648.242
Median Absolute Deviation (MAD)1591
Skewness0.10937815
Sum7.1307877 × 109
Variance2338627.2
MonotonicityNot monotonic
2024-03-30T02:48:49.707542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 27466
 
4.9%
11298 24071
 
4.3%
11292 23939
 
4.2%
13930 20352
 
3.6%
11057 16705
 
3.0%
12889 16237
 
2.9%
14107 15803
 
2.8%
12892 15744
 
2.8%
13204 14333
 
2.5%
12953 13268
 
2.4%
Other values (322) 375859
66.7%
ValueCountFrequency (%)
10135 374
 
0.1%
10136 141
 
< 0.1%
10140 2005
0.4%
10141 59
 
< 0.1%
10146 60
 
< 0.1%
10155 87
 
< 0.1%
10157 149
 
< 0.1%
10158 324
 
0.1%
10165 9
 
< 0.1%
10170 57
 
< 0.1%
ValueCountFrequency (%)
16869 136
 
< 0.1%
16218 144
 
< 0.1%
15991 58
 
< 0.1%
15919 986
0.2%
15841 58
 
< 0.1%
15624 642
0.1%
15607 60
 
< 0.1%
15582 51
 
< 0.1%
15569 51
 
< 0.1%
15412 1154
0.2%

DEST
Text

Distinct332
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:50.466244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1691331
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTL
2nd rowSYR
3rd rowLGA
4th rowTYS
5th rowORF
ValueCountFrequency (%)
atl 27466
 
4.9%
dfw 24071
 
4.3%
den 23939
 
4.2%
ord 20352
 
3.6%
clt 16705
 
3.0%
las 16237
 
2.9%
phx 15803
 
2.8%
lax 15744
 
2.8%
mco 14333
 
2.5%
lga 13268
 
2.4%
Other values (322) 375859
66.7%
2024-03-30T02:48:51.410100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 192698
 
11.4%
L 157357
 
9.3%
S 144524
 
8.5%
D 131418
 
7.8%
T 90204
 
5.3%
O 85956
 
5.1%
C 85402
 
5.0%
M 76276
 
4.5%
F 70498
 
4.2%
W 66386
 
3.9%
Other values (16) 590612
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 192698
 
11.4%
L 157357
 
9.3%
S 144524
 
8.5%
D 131418
 
7.8%
T 90204
 
5.3%
O 85956
 
5.1%
C 85402
 
5.0%
M 76276
 
4.5%
F 70498
 
4.2%
W 66386
 
3.9%
Other values (16) 590612
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 192698
 
11.4%
L 157357
 
9.3%
S 144524
 
8.5%
D 131418
 
7.8%
T 90204
 
5.3%
O 85956
 
5.1%
C 85402
 
5.0%
M 76276
 
4.5%
F 70498
 
4.2%
W 66386
 
3.9%
Other values (16) 590612
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 192698
 
11.4%
L 157357
 
9.3%
S 144524
 
8.5%
D 131418
 
7.8%
T 90204
 
5.3%
O 85956
 
5.1%
C 85402
 
5.0%
M 76276
 
4.5%
F 70498
 
4.2%
W 66386
 
3.9%
Other values (16) 590612
34.9%
Distinct326
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:51.965803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.06512
Min length8

Characters and Unicode

Total characters7365814
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSt. Louis, MO
2nd rowSyracuse, NY
3rd rowNew York, NY
4th rowKnoxville, TN
5th rowNorfolk, VA
ValueCountFrequency (%)
ca 61126
 
4.6%
tx 60564
 
4.6%
fl 52286
 
4.0%
ny 30494
 
2.3%
san 29561
 
2.2%
ga 29387
 
2.2%
il 28116
 
2.1%
new 27962
 
2.1%
atlanta 27466
 
2.1%
chicago 27098
 
2.1%
Other values (396) 941281
71.6%
2024-03-30T02:48:52.839864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
751564
 
10.2%
, 563777
 
7.7%
a 563060
 
7.6%
o 406074
 
5.5%
e 389229
 
5.3%
n 360067
 
4.9%
t 351380
 
4.8%
l 326632
 
4.4%
i 279468
 
3.8%
r 268261
 
3.6%
Other values (46) 3106302
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7365814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
751564
 
10.2%
, 563777
 
7.7%
a 563060
 
7.6%
o 406074
 
5.5%
e 389229
 
5.3%
n 360067
 
4.9%
t 351380
 
4.8%
l 326632
 
4.4%
i 279468
 
3.8%
r 268261
 
3.6%
Other values (46) 3106302
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7365814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
751564
 
10.2%
, 563777
 
7.7%
a 563060
 
7.6%
o 406074
 
5.5%
e 389229
 
5.3%
n 360067
 
4.9%
t 351380
 
4.8%
l 326632
 
4.4%
i 279468
 
3.8%
r 268261
 
3.6%
Other values (46) 3106302
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7365814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
751564
 
10.2%
, 563777
 
7.7%
a 563060
 
7.6%
o 406074
 
5.5%
e 389229
 
5.3%
n 360067
 
4.9%
t 351380
 
4.8%
l 326632
 
4.4%
i 279468
 
3.8%
r 268261
 
3.6%
Other values (46) 3106302
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:53.245908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.139165
Min length4

Characters and Unicode

Total characters4588674
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissouri
2nd rowNew York
3rd rowNew York
4th rowTennessee
5th rowVirginia
ValueCountFrequency (%)
california 61126
 
9.5%
texas 60564
 
9.4%
florida 52286
 
8.1%
new 44469
 
6.9%
york 30494
 
4.7%
georgia 29387
 
4.6%
carolina 29293
 
4.5%
illinois 28116
 
4.4%
colorado 26036
 
4.0%
north 25928
 
4.0%
Other values (51) 257802
39.9%
2024-03-30T02:48:53.869981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 619634
13.5%
i 515141
 
11.2%
o 441630
 
9.6%
n 336979
 
7.3%
r 336923
 
7.3%
e 280857
 
6.1%
s 258402
 
5.6%
l 255067
 
5.6%
C 118131
 
2.6%
d 114188
 
2.5%
Other values (37) 1311722
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4588674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 619634
13.5%
i 515141
 
11.2%
o 441630
 
9.6%
n 336979
 
7.3%
r 336923
 
7.3%
e 280857
 
6.1%
s 258402
 
5.6%
l 255067
 
5.6%
C 118131
 
2.6%
d 114188
 
2.5%
Other values (37) 1311722
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4588674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 619634
13.5%
i 515141
 
11.2%
o 441630
 
9.6%
n 336979
 
7.3%
r 336923
 
7.3%
e 280857
 
6.1%
s 258402
 
5.6%
l 255067
 
5.6%
C 118131
 
2.6%
d 114188
 
2.5%
Other values (37) 1311722
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4588674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 619634
13.5%
i 515141
 
11.2%
o 441630
 
9.6%
n 336979
 
7.3%
r 336923
 
7.3%
e 280857
 
6.1%
s 258402
 
5.6%
l 255067
 
5.6%
C 118131
 
2.6%
d 114188
 
2.5%
Other values (37) 1311722
28.6%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.813401
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:54.242028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median45
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.643483
Coefficient of variation (CV)0.48607608
Kurtosis-1.3228185
Mean54.813401
Median Absolute Deviation (MAD)23
Skewness-0.032891431
Sum30902535
Variance709.8752
MonotonicityNot monotonic
2024-03-30T02:48:54.553908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 61126
 
10.8%
74 60564
 
10.7%
33 52286
 
9.3%
22 30494
 
5.4%
34 29387
 
5.2%
41 28116
 
5.0%
82 26036
 
4.6%
36 24548
 
4.4%
38 18747
 
3.3%
81 18089
 
3.2%
Other values (42) 214384
38.0%
ValueCountFrequency (%)
1 2440
 
0.4%
2 10588
1.9%
3 2929
 
0.5%
4 456
 
0.1%
5 99
 
< 0.1%
11 1676
 
0.3%
12 988
 
0.2%
13 11237
2.0%
14 502
 
0.1%
15 1050
 
0.2%
ValueCountFrequency (%)
93 15305
 
2.7%
92 6420
 
1.1%
91 61126
10.8%
88 542
 
0.1%
87 9416
 
1.7%
86 2193
 
0.4%
85 17844
 
3.2%
84 1640
 
0.3%
83 2343
 
0.4%
82 26036
4.6%

CRS_DEP_TIME
Real number (ℝ)

Distinct1263
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1326.6317
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:54.957840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1905
median1320
Q31738
95-th percentile2128
Maximum2359
Range2358
Interquartile range (IQR)833

Descriptive statistics

Standard deviation495.54703
Coefficient of variation (CV)0.37353776
Kurtosis-1.0671185
Mean1326.6317
Median Absolute Deviation (MAD)415
Skewness0.083275995
Sum7.4792444 × 108
Variance245566.86
MonotonicityNot monotonic
2024-03-30T02:48:55.250722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 12135
 
2.2%
700 9558
 
1.7%
800 5929
 
1.1%
900 3692
 
0.7%
630 3597
 
0.6%
730 3539
 
0.6%
1000 3521
 
0.6%
830 3207
 
0.6%
615 3005
 
0.5%
1100 2906
 
0.5%
Other values (1253) 512688
90.9%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 7
 
< 0.1%
5 4
 
< 0.1%
6 3
 
< 0.1%
7 5
 
< 0.1%
8 28
< 0.1%
9 20
< 0.1%
10 11
 
< 0.1%
ValueCountFrequency (%)
2359 1029
0.2%
2358 42
 
< 0.1%
2357 29
 
< 0.1%
2356 89
 
< 0.1%
2355 168
 
< 0.1%
2354 51
 
< 0.1%
2353 7
 
< 0.1%
2352 38
 
< 0.1%
2351 43
 
< 0.1%
2350 138
 
< 0.1%

DEP_TIME
Real number (ℝ)

Distinct1420
Distinct (%)0.3%
Missing770
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1326.3436
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:55.539662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile557
Q1906
median1322
Q31742
95-th percentile2133
Maximum2400
Range2399
Interquartile range (IQR)836

Descriptive statistics

Standard deviation504.75178
Coefficient of variation (CV)0.38055884
Kurtosis-1.0249768
Mean1326.3436
Median Absolute Deviation (MAD)418
Skewness0.046381843
Sum7.4674072 × 108
Variance254774.36
MonotonicityNot monotonic
2024-03-30T02:48:55.832518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1543
 
0.3%
557 1439
 
0.3%
556 1372
 
0.2%
558 1321
 
0.2%
554 1279
 
0.2%
655 1270
 
0.2%
559 1200
 
0.2%
656 1168
 
0.2%
658 1162
 
0.2%
654 1154
 
0.2%
Other values (1410) 550099
97.6%
ValueCountFrequency (%)
1 59
< 0.1%
2 51
< 0.1%
3 37
< 0.1%
4 55
< 0.1%
5 59
< 0.1%
6 39
< 0.1%
7 53
< 0.1%
8 49
< 0.1%
9 43
< 0.1%
10 40
< 0.1%
ValueCountFrequency (%)
2400 45
 
< 0.1%
2359 71
< 0.1%
2358 83
< 0.1%
2357 77
< 0.1%
2356 109
< 0.1%
2355 115
< 0.1%
2354 124
< 0.1%
2353 108
< 0.1%
2352 118
< 0.1%
2351 96
< 0.1%

DEP_DELAY
Real number (ℝ)

ZEROS 

Distinct955
Distinct (%)0.2%
Missing776
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.8737231
Minimum-72
Maximum3518
Zeros27141
Zeros (%)4.8%
Negative364200
Negative (%)64.6%
Memory size4.3 MiB
2024-03-30T02:48:56.472845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-72
5-th percentile-10
Q1-6
median-3
Q33
95-th percentile47
Maximum3518
Range3590
Interquartile range (IQR)9

Descriptive statistics

Standard deviation41.927209
Coefficient of variation (CV)7.1380976
Kurtosis539.72531
Mean5.8737231
Median Absolute Deviation (MAD)4
Skewness16.520354
Sum3306912
Variance1757.8909
MonotonicityNot monotonic
2024-03-30T02:48:57.034452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 47332
 
8.4%
-4 43658
 
7.7%
-3 41157
 
7.3%
-6 38620
 
6.9%
-2 36907
 
6.5%
-7 33511
 
5.9%
-1 32584
 
5.8%
0 27141
 
4.8%
-8 26875
 
4.8%
-9 19751
 
3.5%
Other values (945) 215465
38.2%
ValueCountFrequency (%)
-72 1
 
< 0.1%
-44 2
< 0.1%
-43 1
 
< 0.1%
-42 2
< 0.1%
-41 1
 
< 0.1%
-39 2
< 0.1%
-38 1
 
< 0.1%
-37 2
< 0.1%
-36 4
< 0.1%
-35 1
 
< 0.1%
ValueCountFrequency (%)
3518 1
< 0.1%
2939 1
< 0.1%
2454 1
< 0.1%
2432 1
< 0.1%
2379 1
< 0.1%
2344 1
< 0.1%
2306 1
< 0.1%
2295 1
< 0.1%
1935 1
< 0.1%
1909 1
< 0.1%

TAXI_OUT
Real number (ℝ)

Distinct138
Distinct (%)< 0.1%
Missing801
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean17.132189
Minimum1
Maximum269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:57.362122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q320
95-th percentile32
Maximum269
Range268
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.1887769
Coefficient of variation (CV)0.47797611
Kurtosis16.058892
Mean17.132189
Median Absolute Deviation (MAD)4
Skewness2.7057902
Sum9645011
Variance67.056068
MonotonicityNot monotonic
2024-03-30T02:48:57.678881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 46344
 
8.2%
12 46257
 
8.2%
14 43618
 
7.7%
11 42331
 
7.5%
15 40216
 
7.1%
16 35471
 
6.3%
10 34297
 
6.1%
17 30801
 
5.5%
18 26729
 
4.7%
9 23235
 
4.1%
Other values (128) 193677
34.4%
ValueCountFrequency (%)
1 21
 
< 0.1%
2 27
 
< 0.1%
3 52
 
< 0.1%
4 171
 
< 0.1%
5 519
 
0.1%
6 2070
 
0.4%
7 5860
 
1.0%
8 12720
 
2.3%
9 23235
4.1%
10 34297
6.1%
ValueCountFrequency (%)
269 1
 
< 0.1%
177 1
 
< 0.1%
162 1
 
< 0.1%
149 1
 
< 0.1%
143 1
 
< 0.1%
142 1
 
< 0.1%
141 3
< 0.1%
139 2
< 0.1%
138 1
 
< 0.1%
137 1
 
< 0.1%

TAXI_IN
Real number (ℝ)

Distinct110
Distinct (%)< 0.1%
Missing869
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean7.858563
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:58.002401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile18
Maximum132
Range131
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.6667585
Coefficient of variation (CV)0.72109348
Kurtosis22.015745
Mean7.858563
Median Absolute Deviation (MAD)2
Skewness3.387098
Sum4423648
Variance32.112152
MonotonicityNot monotonic
2024-03-30T02:48:58.363383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 85959
15.2%
5 81758
14.5%
6 67439
12.0%
7 54795
9.7%
3 48992
8.7%
8 42536
7.5%
9 33052
 
5.9%
10 25786
 
4.6%
11 19863
 
3.5%
12 15811
 
2.8%
Other values (100) 86917
15.4%
ValueCountFrequency (%)
1 717
 
0.1%
2 11697
 
2.1%
3 48992
8.7%
4 85959
15.2%
5 81758
14.5%
6 67439
12.0%
7 54795
9.7%
8 42536
7.5%
9 33052
 
5.9%
10 25786
 
4.6%
ValueCountFrequency (%)
132 1
< 0.1%
131 1
< 0.1%
126 1
< 0.1%
125 1
< 0.1%
118 1
< 0.1%
115 1
< 0.1%
111 2
< 0.1%
107 1
< 0.1%
105 1
< 0.1%
104 2
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1396
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1488.1848
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:58.699032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile720
Q11100
median1515
Q31925
95-th percentile2259
Maximum2359
Range2358
Interquartile range (IQR)825

Descriptive statistics

Standard deviation523.64405
Coefficient of variation (CV)0.35186761
Kurtosis-0.45728531
Mean1488.1848
Median Absolute Deviation (MAD)412
Skewness-0.29822599
Sum8.3900439 × 108
Variance274203.09
MonotonicityNot monotonic
2024-03-30T02:48:59.005749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 2376
 
0.4%
2200 2043
 
0.4%
1400 1824
 
0.3%
1810 1791
 
0.3%
1855 1688
 
0.3%
2000 1675
 
0.3%
1005 1587
 
0.3%
1500 1583
 
0.3%
905 1566
 
0.3%
2150 1564
 
0.3%
Other values (1386) 546080
96.9%
ValueCountFrequency (%)
1 55
 
< 0.1%
2 107
 
< 0.1%
3 125
 
< 0.1%
4 210
< 0.1%
5 467
0.1%
6 220
< 0.1%
7 207
< 0.1%
8 92
 
< 0.1%
9 89
 
< 0.1%
10 487
0.1%
ValueCountFrequency (%)
2359 2376
0.4%
2358 783
 
0.1%
2357 822
 
0.1%
2356 651
 
0.1%
2355 1195
0.2%
2354 518
 
0.1%
2353 361
 
0.1%
2352 531
 
0.1%
2351 367
 
0.1%
2350 764
 
0.1%

ARR_TIME
Real number (ℝ)

Distinct1440
Distinct (%)0.3%
Missing869
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1469.3344
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:48:59.302335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile657
Q11048
median1506
Q31919
95-th percentile2252
Maximum2400
Range2399
Interquartile range (IQR)871

Descriptive statistics

Standard deviation535.05867
Coefficient of variation (CV)0.36415038
Kurtosis-0.42290291
Mean1469.3344
Median Absolute Deviation (MAD)424
Skewness-0.33420864
Sum8.2710008 × 108
Variance286287.78
MonotonicityNot monotonic
2024-03-30T02:48:59.733794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1638 654
 
0.1%
1646 652
 
0.1%
1857 651
 
0.1%
1653 651
 
0.1%
1637 640
 
0.1%
1704 631
 
0.1%
1647 630
 
0.1%
1627 627
 
0.1%
1626 625
 
0.1%
2142 625
 
0.1%
Other values (1430) 556522
98.7%
(Missing) 869
 
0.2%
ValueCountFrequency (%)
1 334
0.1%
2 281
< 0.1%
3 312
0.1%
4 295
0.1%
5 321
0.1%
6 278
< 0.1%
7 257
< 0.1%
8 298
0.1%
9 286
0.1%
10 267
< 0.1%
ValueCountFrequency (%)
2400 309
0.1%
2359 333
0.1%
2358 333
0.1%
2357 369
0.1%
2356 360
0.1%
2355 369
0.1%
2354 373
0.1%
2353 380
0.1%
2352 376
0.1%
2351 370
0.1%

ARR_DELAY
Real number (ℝ)

ZEROS 

Distinct976
Distinct (%)0.2%
Missing1364
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean-1.2563383
Minimum-83
Maximum3502
Zeros10405
Zeros (%)1.8%
Negative391748
Negative (%)69.5%
Memory size4.3 MiB
2024-03-30T02:49:00.136415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-83
5-th percentile-29
Q1-17
median-9
Q33
95-th percentile44
Maximum3502
Range3585
Interquartile range (IQR)20

Descriptive statistics

Standard deviation43.52781
Coefficient of variation (CV)-34.646567
Kurtosis467.43364
Mean-1.2563383
Median Absolute Deviation (MAD)9
Skewness14.802206
Sum-706581
Variance1894.6702
MonotonicityNot monotonic
2024-03-30T02:49:00.528627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12 17300
 
3.1%
-10 17211
 
3.1%
-13 17091
 
3.0%
-14 16996
 
3.0%
-11 16946
 
3.0%
-9 16697
 
3.0%
-15 16483
 
2.9%
-8 16057
 
2.8%
-16 15727
 
2.8%
-7 15375
 
2.7%
Other values (966) 396530
70.3%
ValueCountFrequency (%)
-83 1
 
< 0.1%
-76 1
 
< 0.1%
-75 2
 
< 0.1%
-73 1
 
< 0.1%
-72 1
 
< 0.1%
-71 2
 
< 0.1%
-70 3
< 0.1%
-69 4
< 0.1%
-68 3
< 0.1%
-67 5
< 0.1%
ValueCountFrequency (%)
3502 1
< 0.1%
2928 1
< 0.1%
2518 1
< 0.1%
2408 1
< 0.1%
2362 1
< 0.1%
2325 1
< 0.1%
2319 1
< 0.1%
2281 1
< 0.1%
1920 1
< 0.1%
1918 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0.0
562958 
1.0
 
819

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1691331
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 562958
99.9%
1.0 819
 
0.1%

Length

2024-03-30T02:49:00.883287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:49:01.124124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 562958
99.9%
1.0 819
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1126735
66.6%
. 563777
33.3%
1 819
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1126735
66.6%
. 563777
33.3%
1 819
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1126735
66.6%
. 563777
33.3%
1 819
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1126735
66.6%
. 563777
33.3%
1 819
 
< 0.1%

CANCELLATION_CODE
Categorical

MISSING 

Distinct3
Distinct (%)0.4%
Missing562958
Missing (%)99.9%
Memory size4.3 MiB
A
522 
B
258 
C
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters819
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 522
 
0.1%
B 258
 
< 0.1%
C 39
 
< 0.1%
(Missing) 562958
99.9%

Length

2024-03-30T02:49:01.379050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:49:01.644805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
a 522
63.7%
b 258
31.5%
c 39
 
4.8%

Most occurring characters

ValueCountFrequency (%)
A 522
63.7%
B 258
31.5%
C 39
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 819
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 522
63.7%
B 258
31.5%
C 39
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 819
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 522
63.7%
B 258
31.5%
C 39
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 819
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 522
63.7%
B 258
31.5%
C 39
 
4.8%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0.0
563232 
1.0
 
545

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1691331
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 563232
99.9%
1.0 545
 
0.1%

Length

2024-03-30T02:49:01.953504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:49:02.230613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 563232
99.9%
1.0 545
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1127009
66.6%
. 563777
33.3%
1 545
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1127009
66.6%
. 563777
33.3%
1 545
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1127009
66.6%
. 563777
33.3%
1 545
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1691331
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1127009
66.6%
. 563777
33.3%
1 545
 
< 0.1%

AIR_TIME
Real number (ℝ)

Distinct628
Distinct (%)0.1%
Missing1364
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean114.83977
Minimum8
Maximum690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:49:02.520307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile36
Q163
median98
Q3144
95-th percentile267
Maximum690
Range682
Interquartile range (IQR)81

Descriptive statistics

Standard deviation70.308165
Coefficient of variation (CV)0.61222836
Kurtosis2.654434
Mean114.83977
Median Absolute Deviation (MAD)39
Skewness1.4374634
Sum64587381
Variance4943.2381
MonotonicityNot monotonic
2024-03-30T02:49:02.937074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 4747
 
0.8%
58 4677
 
0.8%
63 4650
 
0.8%
64 4647
 
0.8%
59 4621
 
0.8%
66 4617
 
0.8%
67 4607
 
0.8%
65 4604
 
0.8%
60 4601
 
0.8%
62 4580
 
0.8%
Other values (618) 516062
91.5%
ValueCountFrequency (%)
8 3
 
< 0.1%
9 21
 
< 0.1%
10 15
 
< 0.1%
11 3
 
< 0.1%
12 3
 
< 0.1%
13 1
 
< 0.1%
14 9
 
< 0.1%
15 38
 
< 0.1%
16 86
< 0.1%
17 171
< 0.1%
ValueCountFrequency (%)
690 1
< 0.1%
682 1
< 0.1%
664 2
< 0.1%
663 1
< 0.1%
659 1
< 0.1%
658 1
< 0.1%
657 1
< 0.1%
656 2
< 0.1%
654 2
< 0.1%
652 2
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct752
Distinct (%)1.0%
Missing487430
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean25.206112
Minimum0
Maximum3502
Zeros31823
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:49:03.338172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q323
95-th percentile102
Maximum3502
Range3502
Interquartile range (IQR)23

Descriptive statistics

Standard deviation77.057629
Coefficient of variation (CV)3.057101
Kurtosis233.68573
Mean25.206112
Median Absolute Deviation (MAD)5
Skewness11.626472
Sum1924411
Variance5937.8781
MonotonicityNot monotonic
2024-03-30T02:49:03.744176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31823
 
5.6%
1 1564
 
0.3%
2 1496
 
0.3%
3 1469
 
0.3%
6 1417
 
0.3%
4 1398
 
0.2%
15 1386
 
0.2%
7 1357
 
0.2%
5 1273
 
0.2%
16 1242
 
0.2%
Other values (742) 31922
 
5.7%
(Missing) 487430
86.5%
ValueCountFrequency (%)
0 31823
5.6%
1 1564
 
0.3%
2 1496
 
0.3%
3 1469
 
0.3%
4 1398
 
0.2%
5 1273
 
0.2%
6 1417
 
0.3%
7 1357
 
0.2%
8 1234
 
0.2%
9 1215
 
0.2%
ValueCountFrequency (%)
3502 1
< 0.1%
2928 1
< 0.1%
2454 1
< 0.1%
2408 1
< 0.1%
2362 1
< 0.1%
2241 1
< 0.1%
1891 1
< 0.1%
1838 1
< 0.1%
1795 1
< 0.1%
1730 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct343
Distinct (%)0.4%
Missing487430
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean2.2251562
Minimum0
Maximum1860
Zeros73995
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:49:04.223064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1860
Range1860
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.242073
Coefficient of variation (CV)12.242769
Kurtosis979.58982
Mean2.2251562
Median Absolute Deviation (MAD)0
Skewness26.307175
Sum169884
Variance742.13053
MonotonicityNot monotonic
2024-03-30T02:49:04.722468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73995
 
13.1%
10 66
 
< 0.1%
7 57
 
< 0.1%
15 56
 
< 0.1%
16 51
 
< 0.1%
8 51
 
< 0.1%
6 50
 
< 0.1%
4 49
 
< 0.1%
17 47
 
< 0.1%
2 47
 
< 0.1%
Other values (333) 1878
 
0.3%
(Missing) 487430
86.5%
ValueCountFrequency (%)
0 73995
13.1%
1 46
 
< 0.1%
2 47
 
< 0.1%
3 45
 
< 0.1%
4 49
 
< 0.1%
5 40
 
< 0.1%
6 50
 
< 0.1%
7 57
 
< 0.1%
8 51
 
< 0.1%
9 45
 
< 0.1%
ValueCountFrequency (%)
1860 1
< 0.1%
1488 1
< 0.1%
1483 1
< 0.1%
1018 1
< 0.1%
1014 1
< 0.1%
953 2
< 0.1%
941 1
< 0.1%
934 1
< 0.1%
913 1
< 0.1%
905 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct268
Distinct (%)0.4%
Missing487430
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean9.605551
Minimum0
Maximum1263
Zeros38489
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:49:05.176309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q315
95-th percentile37
Maximum1263
Range1263
Interquartile range (IQR)15

Descriptive statistics

Standard deviation20.89399
Coefficient of variation (CV)2.1751995
Kurtosis447.19533
Mean9.605551
Median Absolute Deviation (MAD)0
Skewness13.486835
Sum733355
Variance436.5588
MonotonicityNot monotonic
2024-03-30T02:49:05.754667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38489
 
6.8%
1 2524
 
0.4%
15 1869
 
0.3%
16 1597
 
0.3%
2 1537
 
0.3%
17 1499
 
0.3%
3 1482
 
0.3%
4 1402
 
0.2%
18 1335
 
0.2%
5 1322
 
0.2%
Other values (258) 23291
 
4.1%
(Missing) 487430
86.5%
ValueCountFrequency (%)
0 38489
6.8%
1 2524
 
0.4%
2 1537
 
0.3%
3 1482
 
0.3%
4 1402
 
0.2%
5 1322
 
0.2%
6 1221
 
0.2%
7 1180
 
0.2%
8 1122
 
0.2%
9 1061
 
0.2%
ValueCountFrequency (%)
1263 1
< 0.1%
950 1
< 0.1%
903 1
< 0.1%
785 1
< 0.1%
719 2
< 0.1%
677 1
< 0.1%
652 1
< 0.1%
634 1
< 0.1%
590 1
< 0.1%
584 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct84
Distinct (%)0.1%
Missing487430
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean0.17798997
Minimum0
Maximum234
Zeros75722
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:49:06.361625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum234
Range234
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7023469
Coefficient of variation (CV)15.18258
Kurtosis1299.1604
Mean0.17798997
Median Absolute Deviation (MAD)0
Skewness28.019224
Sum13589
Variance7.3026785
MonotonicityNot monotonic
2024-03-30T02:49:06.796794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75722
 
13.4%
15 33
 
< 0.1%
9 31
 
< 0.1%
20 31
 
< 0.1%
16 23
 
< 0.1%
21 22
 
< 0.1%
10 22
 
< 0.1%
8 21
 
< 0.1%
2 20
 
< 0.1%
19 19
 
< 0.1%
Other values (74) 403
 
0.1%
(Missing) 487430
86.5%
ValueCountFrequency (%)
0 75722
13.4%
1 13
 
< 0.1%
2 20
 
< 0.1%
3 16
 
< 0.1%
4 12
 
< 0.1%
5 16
 
< 0.1%
6 15
 
< 0.1%
7 16
 
< 0.1%
8 21
 
< 0.1%
9 31
 
< 0.1%
ValueCountFrequency (%)
234 1
< 0.1%
134 1
< 0.1%
124 1
< 0.1%
114 1
< 0.1%
110 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
97 1
< 0.1%
96 1
< 0.1%
92 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct556
Distinct (%)0.7%
Missing487430
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean22.149292
Minimum0
Maximum2325
Zeros40437
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T02:49:07.203099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile99
Maximum2325
Range2325
Interquartile range (IQR)25

Descriptive statistics

Standard deviation55.034199
Coefficient of variation (CV)2.4846933
Kurtosis203.34907
Mean22.149292
Median Absolute Deviation (MAD)0
Skewness9.9025622
Sum1691032
Variance3028.763
MonotonicityNot monotonic
2024-03-30T02:49:07.573559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40437
 
7.2%
16 1038
 
0.2%
15 1033
 
0.2%
17 946
 
0.2%
18 920
 
0.2%
20 840
 
0.1%
19 805
 
0.1%
21 743
 
0.1%
14 741
 
0.1%
13 695
 
0.1%
Other values (546) 28149
 
5.0%
(Missing) 487430
86.5%
ValueCountFrequency (%)
0 40437
7.2%
1 522
 
0.1%
2 524
 
0.1%
3 481
 
0.1%
4 530
 
0.1%
5 536
 
0.1%
6 623
 
0.1%
7 607
 
0.1%
8 571
 
0.1%
9 615
 
0.1%
ValueCountFrequency (%)
2325 1
< 0.1%
2277 1
< 0.1%
1880 1
< 0.1%
1695 1
< 0.1%
1656 1
< 0.1%
1388 1
< 0.1%
1295 1
< 0.1%
1234 1
< 0.1%
1217 1
< 0.1%
1201 1
< 0.1%

Interactions

2024-03-30T02:48:25.365894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:28.442283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:34.115071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:40.069205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:46.223005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:52.010672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:59.101977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:05.202644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:12.182948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:18.534531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:25.349214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:31.423250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:37.891870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:44.622126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:50.864374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:57.050689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:03.492171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:09.298294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:14.633041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:20.233520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:25.600290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:28.852100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:34.557567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:40.390119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:46.535579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:52.490953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:59.413223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:05.499783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:12.654199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:18.841324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:25.660780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:31.754005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:38.271853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:44.971178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:51.236751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:57.355595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:03.745233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:09.548521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:14.880207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:20.496345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:25.819190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:29.099274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:34.838835image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:40.669179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:46.814188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:52.848183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:59.713331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:05.753269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:12.959790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:19.124691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:25.970079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:32.094653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:38.785229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:45.284799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:51.568199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:57.686866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:03.988661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:09.782023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:15.123020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:20.769830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:26.105730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:29.350207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:35.175954image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:40.998256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:47.104725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:53.322961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:00.086901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:06.165103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:13.312182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:19.477578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:26.333517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:32.485395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:39.148731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:45.624711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:51.906059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:58.030218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:04.295156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:10.100627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:15.403174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:21.061363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:26.344218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:29.576294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:35.464268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:41.287498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:47.379565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:53.800637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:00.417270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:06.642962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:13.611769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:20.037675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:26.615346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:32.783066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:39.514881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:45.931788image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:52.242980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:58.505849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:04.540876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:10.379598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:15.626644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:21.308313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:26.584914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:29.831101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:35.781567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:41.634253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:47.681566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:54.268768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:00.710002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:07.010827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:13.972774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:20.435888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:26.939037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:33.125280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:39.910104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:46.281986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:52.571369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:58.830763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:04.881328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:10.659050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:15.855263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:21.631237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:26.807362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:30.137782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:36.112437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:41.927233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:47.976787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:54.599502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:00.997132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:07.336621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:14.329996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:20.782560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:27.217304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:33.441587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:40.269224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:46.569454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:52.861749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:59.135120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:05.130936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:10.899081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:16.106155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:21.858489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:27.028815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:30.432638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:36.405985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:42.271146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:48.287581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:54.953294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:01.299078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:07.673282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:14.628017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:21.123385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:27.523063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:33.754228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:40.725440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:46.856938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:53.188385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:59.441752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:05.446563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:11.141926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:16.342656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:22.117978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:27.261124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:30.719189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:36.683768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:42.572435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:48.590659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:55.297252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:01.588412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:08.047550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:14.963883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:21.463249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:27.816032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:34.087129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:41.042730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:47.190445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:53.513255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T02:47:56.493122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:03.014294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:08.757593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:14.022729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:19.695460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:24.927585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:30.197253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:33.777522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:39.724130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:45.860710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:51.678902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:46:58.752163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:04.888370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:11.807309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:18.234372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:25.025342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:31.093464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:37.535930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:44.303893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:50.501433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:47:56.715095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:03.258125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:09.033106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:14.383924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:19.951329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:48:25.153021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T02:48:30.858792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T02:48:34.026052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
0111/6/2023 12:00:00 AM9E480012953LGANew York, NYNew York2215016STLSt. Louis, MOMissouri6418461838.0-8.033.06.020462035.0-11.00.0NaN0.0138.0NaNNaNNaNNaNNaN
1111/6/2023 12:00:00 AM9E480312953LGANew York, NYNew York2215096SYRSyracuse, NYNew York2221522142.0-10.017.06.023032250.0-13.00.0NaN0.045.0NaNNaNNaNNaNNaN
2111/6/2023 12:00:00 AM9E480415412TYSKnoxville, TNTennessee5412953LGANew York, NYNew York22619614.0-5.013.07.0829801.0-28.00.0NaN0.087.0NaNNaNNaNNaNNaN
3111/6/2023 12:00:00 AM9E480512953LGANew York, NYNew York2215412TYSKnoxville, TNTennessee5418001752.0-8.013.04.020281955.0-33.00.0NaN0.0106.0NaNNaNNaNNaNNaN
4111/6/2023 12:00:00 AM9E480712478JFKNew York, NYNew York2213931ORFNorfolk, VAVirginia3819401934.0-6.042.06.021212123.02.00.0NaN0.061.0NaNNaNNaNNaNNaN
5111/6/2023 12:00:00 AM9E480812953LGANew York, NYNew York2213931ORFNorfolk, VAVirginia3815291520.0-9.027.05.017091647.0-22.00.0NaN0.055.0NaNNaNNaNNaNNaN
6111/6/2023 12:00:00 AM9E480813931ORFNorfolk, VAVirginia3812953LGANew York, NYNew York2218151806.0-9.051.011.019572005.08.00.0NaN0.057.0NaNNaNNaNNaNNaN
7111/6/2023 12:00:00 AM9E481113198MCIKansas City, MOMissouri6412478JFKNew York, NYNew York22704657.0-7.017.018.010591044.0-15.00.0NaN0.0132.0NaNNaNNaNNaNNaN
8111/6/2023 12:00:00 AM9E481212953LGANew York, NYNew York2213577MYRMyrtle Beach, SCSouth Carolina3720402031.0-9.016.04.022542212.0-42.00.0NaN0.081.0NaNNaNNaNNaNNaN
9111/6/2023 12:00:00 AM9E481312478JFKNew York, NYNew York2213931ORFNorfolk, VAVirginia3813401335.0-5.022.05.015101502.0-8.00.0NaN0.060.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
563767711/26/2023 12:00:00 AMYX583710721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia38855905.010.017.03.011001044.0-16.00.0NaN0.079.0NaNNaNNaNNaNNaN
563768711/26/2023 12:00:00 AMYX583810721BOSBoston, MAMassachusetts1314524RICRichmond, VAVirginia3815101504.0-6.021.08.017081701.0-7.00.0NaN0.088.0NaNNaNNaNNaNNaN
563769711/26/2023 12:00:00 AMYX583814524RICRichmond, VAVirginia3810721BOSBoston, MAMassachusetts1317561749.0-7.010.09.019441917.0-27.00.0NaN0.069.0NaNNaNNaNNaNNaN
563770711/26/2023 12:00:00 AMYX584210721BOSBoston, MAMassachusetts1311066CMHColumbus, OHOhio4413501342.0-8.019.06.016111608.0-3.00.0NaN0.0121.0NaNNaNNaNNaNNaN
563771711/26/2023 12:00:00 AMYX584414492RDURaleigh/Durham, NCNorth Carolina3612953LGANew York, NYNew York22600557.0-3.017.021.0751733.0-18.00.0NaN0.058.0NaNNaNNaNNaNNaN
563772711/26/2023 12:00:00 AMYX584510721BOSBoston, MAMassachusetts1312953LGANew York, NYNew York2219001855.0-5.031.010.020372052.015.00.0NaN0.076.00.00.015.00.00.0
563773711/26/2023 12:00:00 AMYX584712339INDIndianapolis, INIndiana4212953LGANew York, NYNew York2215151508.0-7.010.09.017241654.0-30.00.0NaN0.087.0NaNNaNNaNNaNNaN
563774711/26/2023 12:00:00 AMYX584811066CMHColumbus, OHOhio4410721BOSBoston, MAMassachusetts1312061200.0-6.013.09.014031353.0-10.00.0NaN0.091.0NaNNaNNaNNaNNaN
563775711/26/2023 12:00:00 AMYX584911278DCAWashington, DCVirginia3810721BOSBoston, MAMassachusetts1317001659.0-1.012.011.018511827.0-24.00.0NaN0.065.0NaNNaNNaNNaNNaN
563776711/26/2023 12:00:00 AMYX585011278DCAWashington, DCVirginia3810721BOSBoston, MAMassachusetts1311501142.0-8.012.021.013321319.0-13.00.0NaN0.064.0NaNNaNNaNNaNNaN